_base_ = "ssd300_voc0712.py"
input_size = 512
model = dict(
    backbone=dict(input_size=input_size),
    bbox_head=dict(
        in_channels=(512, 1024, 512, 256, 256, 256, 256),
        anchor_generator=dict(
            input_size=input_size,
            strides=[8, 16, 32, 64, 128, 256, 512],
            basesize_ratio_range=(0.15, 0.9),
            ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]),
        ),
    ),
)
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
    dict(type="LoadImageFromFile", to_float32=True),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="PhotoMetricDistortion",
        brightness_delta=32,
        contrast_range=(0.5, 1.5),
        saturation_range=(0.5, 1.5),
        hue_delta=18,
    ),
    dict(
        type="Expand",
        mean=img_norm_cfg["mean"],
        to_rgb=img_norm_cfg["to_rgb"],
        ratio_range=(1, 4),
    ),
    dict(
        type="MinIoURandomCrop", min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3
    ),
    dict(type="Resize", img_scale=(512, 512), keep_ratio=False),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(512, 512),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=False),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    train=dict(dataset=dict(pipeline=train_pipeline)),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)
